metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: distilbert_toxic
results: []
distilbert_toxic
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5048
- Accuracy: 0.8612
- Precision: 0.8469
- Recall: 0.8195
- F1: 0.8330
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 3407
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.2971 | 1.0 | 4767 | 0.3258 | 0.8675 | 0.8643 | 0.8140 | 0.8384 |
| 0.2798 | 2.0 | 9534 | 0.3120 | 0.8708 | 0.8452 | 0.8498 | 0.8475 |
| 0.1481 | 3.0 | 14301 | 0.3898 | 0.8681 | 0.8466 | 0.8399 | 0.8432 |
| 0.1161 | 4.0 | 19068 | 0.5048 | 0.8612 | 0.8469 | 0.8195 | 0.8330 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0